collaborators

9 papers

cs.CV2026

Recurrent Contrastive Learning for Imbalanced Medical Image Classification

Zhiyuan Zhu, Xinling Meng, Junxuan Yu +15

Medical image classification often suffers from class imbalance due to the inherent disparities in disease incidence. Existing approaches, such as class resampling and loss reweigh…

eess.IV2025

TUS-REC2024: A Challenge to Reconstruct 3D Freehand Ultrasound Without External Tracker

Qi Li, Shaheer U. Saeed, Yuliang Huang +24

Trackerless freehand ultrasound reconstruction aims to reconstruct 3D volumes from sequences of 2D ultrasound images without relying on external tracking systems. By eliminating th…

cs.CV2025

Uncertainty-aware Diffusion and Reinforcement Learning for Joint Plane Localization and Anomaly Diagnosis in 3D Ultrasound

Yuhao Huang, Yueyue Xu, Haoran Dou +4

Congenital uterine anomalies (CUAs) can lead to infertility, miscarriage, preterm birth, and an increased risk of pregnancy complications. Compared to traditional 2D ultrasound (US…

eess.IV2025

MTCNet: Motion and Topology Consistency Guided Learning for Mitral Valve Segmentationin 4D Ultrasound

Rusi Chen, Yuanting Yang, Jiezhi Yao +12

Mitral regurgitation is one of the most prevalent cardiac disorders. Four-dimensional (4D) ultrasound has emerged as the primary imaging modality for assessing dynamic valvular mor…

cs.CV2025

Medical-Knowledge Driven Multiple Instance Learning for Classifying Severe Abdominal Anomalies on Prenatal Ultrasound

Huanwen Liang, Jingxian Xu, Yuanji Zhang +11

Fetal abdominal malformations are serious congenital anomalies that require accurate diagnosis to guide pregnancy management and reduce mortality. Although AI has demonstrated sign…

cs.CV2025

ADAptation: Reconstruction-based Unsupervised Active Learning for Breast Ultrasound Diagnosis

Yaofei Duan, Yuhao Huang, Xin Yang +9

Deep learning-based diagnostic models often suffer performance drops due to distribution shifts between training (source) and test (target) domains. Collecting and labeling suffici…